High-performance liquid chromatographic method for the simultaneous detection of the adulteration of cereal flours with melamine and related triazine by-products ammeline, ammelide, and cyanuric acid
Bibliographic record
Abstract
Melamine has been used for the adulteration of cereal flours in order to increase their apparent protein content. Crude melamine may contain several by-products, i.e. ammeline, ammelide, and cyanuric acid. The simultaneous analysis of all four chemicals is difficult because of the formation of an insoluble salt between melamine and cyanuric acid. A simple and convenient high-performance liquid chromatography (HPLC) method for the detection of the adulteration of cereal flours with all four chemicals is proposed herein. The precipitate formation between melamine and cyanuric acid was prevented by using alkaline conditions (pH 11-12) for both standards preparation and sample extraction. The method uses matrix-matching, which involves the construction of a calibration curve on a blank (negative control) matrix, which is then used for the quantitation of melamine and by-products in adulterated (positive) samples. Matrix-matching compensates for analyte losses during sample preparation, and for matrix effects. The method was successfully applied to wheat, corn, and rice flours, and is expected to be applicable (with some modifications) to soy flour as well. The method allows for the detection of melamine, ammeline, and ammelide at approximately 5 microg g(-1), and cyanuric acid at approximately 90 microg g(-1) in wheat flour.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".